predict.cv.glmgraph {glmgraph} | R Documentation |
This function makes predictions from a cross-validated glmgraph model,
using the stored "cv.glmgraph"
object, and the optimal value
chosen for lambda1
and lambda2
.
## S3 method for class 'cv.glmgraph' predict(object,X,s=c("lambda1.min","lambda1.1se"), type=c("response", "coefficients","class", "nzeros","link"),...)
object |
Fitted |
X |
Matrix at which predictions are to be made. |
s |
Either |
type |
Type of prediction: |
... |
Other parameters to |
Li Chen <li.chen@emory.edu> , Jun Chen <chen.jun2@emory.edu>
Li Chen. Han Liu. Hongzhe Li. Jun Chen. (2015) Graph-constrained Regularization for Sparse Generalized Linear Models.(Working paper)
cv.glmgraph
,coef.cv.glmgraph
set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 ### construct laplacian matrix from adjacency matrix A <- matrix(rep(0,p*p),p,p) A[1:p1,1:p1] <- 1 A[(p1+1):p,(p1+1):p] <- 1 diag(A) <- 0 btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL ### gaussian Y <- eta+rnorm(n) cv.obj <- cv.glmgraph(X,Y,L) beta.min <- predict(cv.obj,X,type="coefficients")